Probabilistic amplitude shaping (PAS) combines an outer shaping layer with an inner, systematic forward error correction (FEC) layer to close the shaping gap. Proposed for PAS, constant composition distribution matching (CCDM) produces amplitude sequences with a fixed empirical distribution. We show that CCDM suffers from high rate losses for small block lengths, and we propose to use Enumerative Sphere Shaping (ESS) instead. ESS minimizes the rate loss at any block length. Furthermore, we discuss the computational complexity of ESS and demonstrate that it is significantly smaller than shell mapping (SM), which is another method to perform sphere shaping. We then study the choice of design parameters for PAS. Following Wachsmann et al., we show that for a given constellation and target rate, there is an optimum balance between the FEC code rate and the entropy of the Maxwell-Boltzmann distribution that minimizes the gap-to-capacity. Moreover, we demonstrate how to utilize the non-systematic convolutional code from IEEE 802.11 in PAS. Simulations over the additive white Gaussian noise (AWGN) and frequency-selective channels exhibit that ESS is up to 1.6 and 0.7 dB more energy-efficient than uniform signaling at block lengths as small as 96 symbols, respectively, with convolutional and low-density parity-check (LDPC) codes.

The dependency between the Gaussianity of the input distribution for the additive white Gaussian noise (AWGN) channel and the gap-to-capacity is discussed. We show that a set of particular approximations to the Maxwell- Boltzmann (MB) distribution virtually closes most of the shaping gap. We relate these symbol-level distributions to bit-level distributions, and demonstrate that they correspond to keeping some of the amplitude bit-levels uniform and independent of the others. Then we propose partial enumerative sphere shaping (P-ESS) to realize such distributions in the probabilistic amplitude shaping (PAS) framework. Simulations over the AWGN channel exhibit that shaping 2 amplitude bits of 16-ASK have almost the same performance as shaping 3 bits, which is 1.3 dB more power- efficient than uniform signaling at a rate of 3 bit/symbol. In this way, required storage and computational complexity of shaping are reduced by factors of 6 and 3, respectively.

The 3rd Generation Partnership Project (3GPP) has recently established in Rel. 14 a network-assisted resource allocation scheme for vehicular broadcast communications. Such novel paradigm is known as vehicle-to-vehicle (V2V) \textit{mode-3} and consists in eNodeBs engaging only in the distribution of sidelink subchannels among vehicles in coverage. Thereupon, without further intervention of the former, vehicles will broadcast their respective signals directly to their counterparts. Because the allotment of subchannels takes place intermittently to reduce signaling, it must primarily be conflict-free in order not to jeopardize the reception of signals. We have identified four pivotal types of allocation requirements that must be guaranteed: one quality of service (QoS) requirement and three conflict conditions which must be precluded in order to preserve reception reliability. The underlying problem is formulated as a maximization of the system sum-capacity with four types of constraints that must be enforced. In addition, we propose a three-stage suboptimal approach that is cast as multiple independent knapsack problems (MIKPs). We compare the two approaches through simulations and show that the latter formulation can attain acceptable performance at lesser complexity.

Source localization is of pivotal importance in several areas such as wireless sensor networks and Internet of Things (IoT), where the location information can be used for a variety of purposes, e.g. surveillance, monitoring, tracking, etc. Time Difference of Arrival (TDOA) is one of the well- known localization approaches where the source broadcasts a signal and a number of receivers record the arriving time of the transmitted signal. By means of computing the time difference from various receivers, the source location can be estimated. On the other hand, in the recent few years novel optimization algorithms have appeared in the literature for (i) processing big data and for (ii) training deep neural networks. Most of these techniques are enhanced variants of the classical stochastic gradient descent (SGD) but with additional features that promote faster convergence. In this paper, we compare the performance of the classical SGD with the novel techniques mentioned above. In addition, we propose an optimization procedure called RMSProp+AF, which is based on RMSProp algorithm but with the advantage of incorporating adaptation of the decaying factor. We show through simulations that all of these techniques--which are commonly used in the machine learning domain- -can also be successfully applied to signal processing problems and are capable of attaining improved convergence and stability. Finally, it is also shown through simulations that the proposed method can outperform other competing approaches as both its convergence and stability are superior.

In Release 14, the 3rd Generation Partnership Project (3GPP) introduced Cellular Vehicle-to-Everything (C-V2X) mode-4 as a novel disruptive technology to support sidelink vehicular communications in out-of-coverage scenarios. C-V2X mode-4 has been engineered to operate in a distributed manner, wherein vehicles autonomously monitor the received power across sidelink subchannels before selecting one for utilization. By means of such an strategy, vehicles attempt to (i) discover and (ii) reserve subchannels with low interference that may have the potential to maximize the reception likelihood of their own broadcasted safety messages. However, due to dynamicity of the vehicular environment, the subchannels optimality may fluctuate rapidly over time. As a consequence, vehicles are required to make a new selection every few hundreds of milliseconds. In consonance with 3GPP, the subchannel selection phase relies on the linear average of the perceived power intensities on each of the subchannels during a monitoring window. However, in this paper we propose a nonlinear power averaging phase, where the most up-to-date measurements are assigned higher priority via exponential weighting. We show through simulations that the overall system performance can be leveraged in both urban and freeway scenarios. Furthermore, the linear averaging can be considered as a special case of the exponentially-weighted moving average, ensuring backward compatibility with the standardized method. Finally, the 3GPP mode-4 scheduling approach is described in detail.

In Release 14, 3GPP completed a first version of cellular vehicle-to-everything (C-V2X) communications wherein two modalities were introduced. One of these schemes, known as \textit{mode-3}, requires support from eNodeBs in order to realize subchannel scheduling. This paper discusses a graph theoretical approach for semi- persistent scheduling (SPS) in \textit{mode-3} harnessing a sensing mechanism whereby vehicles can monitor signal-to-interference-plus-noise ratio (SINR) levels across sidelink subchannels. eNodeBs request such measurements from vehicles and utilize them to accomplish suitable subchannel assignments. However, since SINR values--herein also referred to as side information--span a wide range, quantization is required. We conclude that 3 bits per vehicle every 100 ms can provide sufficient granularity to maintain appropriate performance without severe degradation. Furthermore, the proposed algorithm is compared against pseudo-random and greedy SPS algorithms.

Conversely to mainstream cellular networks where uplink / downlink data traffic is centrally managed by eN-odeBs, in vehicle-to-vehicle (V2V) broadcast communications mode-3 eNodeBs engage solely in subchannel assignment but ultimately do not intervene in data traffic control. Accordingly, vehicles communicate directly with their counterparts utilizing the allotted subchannels. Due to its loosely controlled one-to-all nature, V2V mode-3 is advantageous for time-critical applications. Nevertheless, it is imperative that the assignment of subchannels is accomplished without conflicts while at the same time satisfying quality of service (QoS) requirements. To the best of our knowledge, there exists no unified framework for V2V mode-3 that contemplates both prevention of allocation conflicts and fulfillment of QoS. Thus, four types of conditions that are of forceful character for attaining QoS-aware conflict-free allocations have been identified: (i) assure differentiated QoS per vehicle, (ii) preclude intra-cluster subframe conflicts, (iii) secure minimal time dispersion of allotted subchannels and (iv) forestall one-hop inter-cluster subchannel conflicts. Such conditions have been systematized and merged in an holistic manner allowing non-complex manipulation to perform subchannel allocation optimization. In addition, we propose a surrogate relaxation of the problem that does not affect optimality provided that certain requisites are satisfied.

In this paper we present a graph-based resource allocation scheme for sidelink broadcast V2V communications. Harnessing available information on geographical position of vehicles and spectrum resources utilization, eNodeBs are capable of allotting the same set of sidelink resources to different vehicles distributed among several communications clusters. Within a communications cluster, it is crucial to prevent time-domain allocation conflicts since vehicles cannot transmit and receive simultaneously, i.e., they must transmit in orthogonal time resources. In this research, we present a solution based on a bipartite graph, where vehicles and spectrum resources are represented by vertices whereas the edges represent the achievable rate in each resource based on the SINR that each vehicle perceives. The aforementioned time orthogonality constraint can be approached by aggregating conflicting vertices into macro-vertices which, in addition, reduces the search complexity. We show mathematically and through simulations that the proposed approach yields an optimal solution. In addition, we provide simulations showing that the proposed method outperforms other competing approaches, specially in scenarios with high vehicular density.

In this paper we present a graph-based resource allocation scheme for sidelink broadcast vehicle-to-vehicle (V2V) communications. Harnessing available information on the geographical position of vehicles and spectrum resources utilization, eNodeBs are capable of allotting the same set of sidelink resources to several different vehicles in order for them to broadcast their signals. Hence, vehicles sharing the same resources would ideally be in different communications clusters for the interference level - generated due to resource repurposing - to be maintained under control. Within a communications cluster, it is crucial that vehicles transmit in orthogonal time resources to prevent conflicts as vehicles - with half-duplex radio interfaces - cannot transmit and receive simultaneously. In this research, we have envisaged a solution based on a bipartite graph, where vehicles and spectrum resources are represented by vertices whereas the edges represent the achievable rate in each resource based on the signal-to-interference-plus-noise ratio (SINR) that vehicles perceive. The aforementioned constraint on time orthogonality of allocated resources can be approached by aggregating conflicting vertices into macro-vertices which, in addition, narrows the search space yielding a solution with computational complexity equivalent to the conventional graph matching problem. We show mathematically and through simulations that the proposed approach yields an optimal solution. In addition, we provide simulations showing that the proposed method outperforms other competing approaches, specially in scenarios with high vehicular density.

In this poster we present a graph-based hierarchical subchannel allocation scheme for V2V sidelink communications in Mode-3. Under this scheme, the eNodeB allocates subchannels for in-coverage vehicles. Then, vehicles will broadcast directly without the eNodeB intervening in the process. Therefore, in each communications cluster, it will become crucial to prevent allocation conflicts in time domain since vehicles will not be able to transmit and receive simultaneously. We present a solution where the time-domain requirement can be enforced through vertex aggregation. Additionally, allocation of subchannels is performed sequentially from the most to the least allocation-constrained cluster. We show through simulations that the proposed approach attains near-optimality.

In a parallel concatenated convolutional code, an information sequence is encoded by a convolutional encoder, and an interleaved version of the information sequence is encoded by another convolutional encoder. We discuss the situation in which we require both convolutional encoders to end in the all-zero state. To do so, we have to split an information word in two parts. One part contains the true information bits, and the second part contains the so-called tail bits, which are special bits with values computed such that both encoders end in the all-zero state. Depending on the interleaver, a different number of tail bits are needed. By using a constructive method, we give a characterization of all interleavers for a prescribed number of tail bits. We explain the method of encoding. In addition, simulations have been carried out to investigate the performance of codes resulting from simultaneous zero-tailing. This shows that simultaneous zero-tailing is similar in performance as compared to previously known zero-tailing methods (but with fewer trellis termination bits) and that it is better than zero-tailing just one of the encoders.

For original paper see ibid., vol.44, p.413-15 (1996 April). The present authors provide counterexamples to the expressions for the minimum Hamming distance of the projection codes in the original paper of Li et al.